



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class      _UpperCAmelCase      (							_SCREAMING_SNAKE_CASE  ):
     UpperCamelCase__		     =					(UnCLIPScheduler,)




     def 		snake_case_		(					self			,    **a__):
         A__					=       {
             '''num_train_timesteps''': 1_0_0_0,
             '''variance_type''': '''fixed_small_log''',
             '''clip_sample''': True,
             '''clip_sample_range''': 1.0,
             '''prediction_type''': '''epsilon''',
         }

         config.update(**__UpperCAmelCase)
         return config




     def 		snake_case_		(					self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=__UpperCAmelCase)




     def 		snake_case_		(					self):
         for variance in ["fixed_small_log", "learned_range"]:
             self.check_over_configs(variance_type=__UpperCAmelCase)




     def 		snake_case_		(					self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=__UpperCAmelCase)




     def 		snake_case_		(					self):
         for clip_sample_range in [1, 5, 1_0, 2_0]:
             self.check_over_configs(clip_sample_range=__UpperCAmelCase)




     def 		snake_case_		(					self):
         for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(prediction_type=__UpperCAmelCase)




     def 		snake_case_		(					self):
         for time_step in [0, 5_0_0, 9_9_9]:
             for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                 if prev_timestep is not None and prev_timestep >= time_step:
                     continue

                 self.check_over_forward(time_step=__UpperCAmelCase			,    prev_timestep=__UpperCAmelCase)




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(variance_type='''fixed_small_log''')
         A__					=       scheduler_class(**__UpperCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0_000e-10)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_5_4_9_6_2_5)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.9_9_9_4_9_8_7)) < 1e-5




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config(variance_type='''learned_range''')
         A__					=       scheduler_class(**__UpperCAmelCase)

         A__					=       0.5

         assert scheduler._get_variance(1			,    predicted_variance=__UpperCAmelCase) - -1_0.1_7_1_2_7_9_0 < 1e-5
         assert scheduler._get_variance(4_8_7			,    predicted_variance=__UpperCAmelCase) - -5.7_9_9_8_0_5_2 < 1e-5
         assert scheduler._get_variance(9_9_9			,    predicted_variance=__UpperCAmelCase) - -0.0_0_1_0_0_1_1 < 1e-5




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config()
         A__					=       scheduler_class(**__UpperCAmelCase)

         A__					=       scheduler.timesteps

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter
         A__					=       torch.manual_seed(0)

         for i, t in enumerate(__UpperCAmelCase):
             # 1. predict noise residual
             A__					=       model(__UpperCAmelCase			,    __UpperCAmelCase)

             # 2. predict previous mean of sample x_t-1
             A__					=       scheduler.step(__UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    generator=__UpperCAmelCase).prev_sample

             A__					=       pred_prev_sample

         A__					=       torch.sum(torch.abs(__UpperCAmelCase))
         A__					=       torch.mean(torch.abs(__UpperCAmelCase))

         assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5) < 1e-2
         assert abs(result_mean.item() - 0.3_2_8_4_7_4_3) < 1e-3




     def 		snake_case_		(					self):
         A__					=       self.scheduler_classes[0]
         A__					=       self.get_scheduler_config()
         A__					=       scheduler_class(**__UpperCAmelCase)

         scheduler.set_timesteps(2_5)

         A__					=       scheduler.timesteps

         A__					=       self.dummy_model()
         A__					=       self.dummy_sample_deter
         A__					=       torch.manual_seed(0)

         for i, t in enumerate(__UpperCAmelCase):
             # 1. predict noise residual
             A__					=       model(__UpperCAmelCase			,    __UpperCAmelCase)

             if i + 1 == timesteps.shape[0]:
                 A__					=       None
             else:
                 A__					=       timesteps[i + 1]

             # 2. predict previous mean of sample x_t-1
             A__					=       scheduler.step(
                 __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			,    prev_timestep=__UpperCAmelCase			,    generator=__UpperCAmelCase).prev_sample

             A__					=       pred_prev_sample

         A__					=       torch.sum(torch.abs(__UpperCAmelCase))
         A__					=       torch.mean(torch.abs(__UpperCAmelCase))

         assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3) < 1e-2
         assert abs(result_mean.item() - 0.3_3_6_2_0_3_8) < 1e-3




     def 		snake_case_		(					self):
         pass




     def 		snake_case_		(					self):
         pass



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






